Data Services Sales Design With Mixed Bundling Strategy: A Multidimensional Adverse Selection Approach

In the era of the Internet of Things (IoT), an immense amount of data is generated from numerous sensors and devices. Data as a service (DaaS) represents a new market whose time has come, and DaaS-based businesses are emerging quickly. Businesses across sectors begin seeing their data not only as fundamentally valuable but economically viable to distribute. Due to the exponential growth of the DaaS market, the current pricing models gradually become less suitable for the selling of data sets. A more sophisticated pricing strategy is needed to unlock the value of that data for the data vendor's (DV's) revenue growth and their customers' benefits such as online service providers (SPs). In this article, we aim to maximize the DV's profits by designing a mixed sales mechanism, which allows the DV to sell data sets separately or bundled. Particularly, we apply a multidimensional adverse selection model from contract theory to model the data set trading between DVs and SPs. The DV's surplus maximization problem is solved in the single-product case first, then extended to the multiproduct case. Furthermore, the analysis of the solution of the pricing strategy in single-product and multiproduct cases is provided. Finally, the simulation results show that the proposed pricing model can improve the DV's profits efficiently.